{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "使用ipython + matplotlib 绘制图形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "#import matplotlib\n",
    "#matplotlib.use('Agg')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#First create some toy data:\n",
    "x = np.linspace(0, 2*np.pi, 400)\n",
    "y = np.sin(x**2)\n",
    "\n",
    "#Creates just a figure and only one subplot\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)\n",
    "ax.set_title('Simple plot')\n",
    "\n",
    "#Creates two subplots and unpacks the output array immediately\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "ax1.plot(x, y)\n",
    "ax1.set_title('Sharing Y axis')\n",
    "ax2.scatter(x, y)\n",
    "\n",
    "#Creates four polar axes, and accesses them through the returned array\n",
    "fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))\n",
    "axes[0, 0].plot(x, y)\n",
    "axes[1, 1].scatter(x, y)\n",
    " \n",
    "#Share a X axis with each column of subplots\n",
    "# plt.subplots(2, 2, sharex='col')\n",
    "\n",
    "#Share a Y axis with each row of subplots\n",
    "# plt.subplots(2, 2, sharey='row')\n",
    " \n",
    "#Share both X and Y axes with all subplots\n",
    "# plt.subplots(2, 2, sharex='all', sharey='all')\n",
    "\n",
    "#Note that this is the same as\n",
    "# plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "\n",
    "#Creates figure number 10 with a single subplot\n",
    "#and clears it if it already exists.\n",
    "# fig, ax=plt.subplots(num=10, clear=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       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\n"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#First create some toy data:\n",
    "x = np.linspace(0, 100, 100)\n",
    "y = np.log2(x+10)\n",
    "\n",
    "plt.plot(x, y, '-')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "name": "python_matplotlib.ipynb",
  "nikola": {
   "category": "",
   "date": "2019-01-15 21:54:29 UTC+08:00",
   "description": "",
   "link": "",
   "slug": "ipython+matplotlib数据展示",
   "tags": "",
   "title": "ipython+matplotlib数据展示",
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}
